class_docstr: 1D convolution layer (e.g. temporal convolution).
  
  This layer creates a convolution kernel that is convolved
  with the layer input over a single spatial (or temporal) dimension
  to produce a tensor of outputs.
  If `use_bias` is True, a bias vector is created and added to the outputs.
  Finally, if `activation` is not `None`,
  it is applied to the outputs as well.
  
  When using this layer as the first layer in a model,
  provide an `input_shape` argument
  (tuple of integers or `None`, e.g.
  `(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
  or `(None, 128)` for variable-length sequences of 128-dimensional vectors.
  
  # Arguments
      filters: Integer, the dimensionality of the output space
          (i.e. the number of output filters in the convolution).
      kernel_size: An integer or tuple/list of a single integer,
          specifying the length of the 1D convolution window.
      strides: An integer or tuple/list of a single integer,
          specifying the stride length of the convolution.
          Specifying any stride value != 1 is incompatible with specifying
          any `dilation_rate` value != 1.
      padding: One of `"valid"` or `"same"` (case-insensitive).
          `"valid"` means "no padding".
          `"same"` results in padding the input such that
          the output has the same length as the original input.
          (see [activations](../activations.md)).
          If you don't specify anything, no activation is applied
          (ie. "linear" activation: `a(x) = x`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix
      bias_initializer: Initializer for the bias vector
      kernel_regularizer: Regularizer function applied to
          the `kernel` weights matrix
      bias_regularizer: Regularizer function applied to the bias vector
  
  # Input shape
      3D tensor with shape: `(batch_size, steps, input_dim)`
  
  # Output shape
      3D tensor with shape: `(batch_size, new_steps, filters)`
      `steps` value might have changed due to padding or strides.
  
  >>> conv1d = Conv1D(12, 4, input_shape=(3, 16))
  creating: createZooKeras2Conv1D
code: def __init__(
      self,
      filters,
      kernel_size,
      strides=1,
      padding="valid",
      activation=None,
      use_bias=True,
      kernel_initializer="glorot_uniform",
      bias_initializer="zero",
      kernel_regularizer=None,
      bias_regularizer=None,
      input_shape=None,
      **kwargs,
  ):
      super(Conv1D, self).__init__(
          None,
          filters,
          kernel_size,
          strides,
          padding,
          activation,
          use_bias,
          kernel_initializer,
          bias_initializer,
          kernel_regularizer,
          bias_regularizer,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/keras2/layers/convolutional.py
imports: import sys
  from zoo.pipeline.api.keras2.base import ZooKeras2Layer
label_desc: Use image node_id 1 to create a new Conv1D object from inherited base classes: ZooKeras2Layer with example: obj = Conv1D(filters, kernel_size, strides, padding, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, input_shape)